A Semiparametric Bayesian Multivariate Model for Survival Probabilities After Acute Myocardial Infarction

In this work, a Bayesian semiparametric multivariate model is fitted to study data related to in-hospital and 60-day survival probabilities of patients admitted to a hospital with ST-elevation myocardial infarction diagnosis. We consider a hierarchical generalized linear model to predict survival probabilities and a process indicator (time of intervention). Poisson-Dirichlet process priors, generalizing the well-known Dirichlet process, are considered for modeling the random-effect distribution of the grouping factor which is the hospital of admission.